Efficient Road Lane Marking Detection with Deep Learning
This addresses the problem of balancing accuracy and computational efficiency for lane detection in ADAS, though it appears incremental as it builds on existing deep learning methods with specific optimizations.
The paper tackles the challenge of efficient lane marking detection for ADAS by proposing a deep convolutional neural network with dilated convolution and a shallower/thinner architecture to reduce computational complexity while maintaining accuracy, achieving promising results on captured road scenes.
Lane mark detection is an important element in the road scene analysis for Advanced Driver Assistant System (ADAS). Limited by the onboard computing power, it is still a challenge to reduce system complexity and maintain high accuracy at the same time. In this paper, we propose a Lane Marking Detector (LMD) using a deep convolutional neural network to extract robust lane marking features. To improve its performance with a target of lower complexity, the dilated convolution is adopted. A shallower and thinner structure is designed to decrease the computational cost. Moreover, we also design post-processing algorithms to construct 3rd-order polynomial models to fit into the curved lanes. Our system shows promising results on the captured road scenes.